Catálogo de publicaciones - libros

In this paper, we propose a naive statistics method for constructing a personalized recommendation system for the Electronic Program Guide (EPG). The idea is based on a primitive approach of N-gram to acquire nouns and compound nouns as prediction features, and then to combine the weighting to predict user favorite programs. Our approach unified feedback process, system can incrementally update the vector of extracted features and their scores. It was proved that our system has good accuracy and dynamically adaptive capability.

We examine an automated mechanism, which allows users to access this information in a structured manner by segmenting unformatted text records into structured elements, annotating these documents using XML tags and using specific query processing techniques. This research is the first step to make an automatic ontology generation system. Therefore, we focus on the explanation how we can automatically extract structure when seeded with a small number of training examples. We propose an approach based on Hidden Markov Models to build a powerful probabilistic model that corroborates multiple sources of information including, the sequence of elements, their length distribution, distinguishing words from the vocabulary and an optional external data dictionary. We introduce two different HMM models for information extraction from different sources such as bibliography and Call for Papers documents as a training dataset. The proposed HMM learn to distinguish the fields, and then extract title, authors, conference / journal names, etc. from the text.

In 2003, Kim applied DCT technique to propose a content-based image copy detection method. He successfully detected the copies with or without modifications, and his method is the first that can detect the copies with water coloring and twirling modifications. However, Kim’s method can only detect copies modified with a 180 degree rotation. When copies are rotated by 90 or 270 degrees, Kim’s method fails to discover them. Also, his method can not deal with the copies with minor rotations like 1 degree or 5 degree rotation, and so on. To conquer this weakness, we apply ellipse track division strategy to extract the features and propose our methods. The experimental results confirm that our proposed method can successfully capture block features of an image even if it is rotated to any degree.

To achieve high quality of push-based information service, in this paper, collaborative filtering and content-based adaptability approaches are surveyed for user-centered personalized information, then based on the above method, we proposed a mixed two-phased recommendation algorithm for high-quality information recommendation, upon which performance evaluations showed that the mixed algorithm is more efficient than pure content-based or collaborative filtering methods, for pure of either approaches is not so efficient for the lack of enough information need information. And moreover we found with large amount registered users, it is necessary and important for the system to serve users in a group mode, which involved merged retrieval issues.

The World Wide Web can be viewed as a gigantic distributed database including millions of interconnected hosts some of which publish information via web servers or peer-to-peer systems. We present here a novel method for the extraction of semantically rich information from the web in a fully automated fashion. We illustrate our approach via a proof-of-concept application which scrutinizes millions of web pages looking for clues as to the trend of the Chinese stock market. We present the outcomes of a 210-day long study which indicates a strong correlation between the information retrieved by our prototype and the actual market behavior.

Support vector machines (SVMs) have been successfully applied to solve nonlinear regression and times series problems. However, the application of SVMs for tourist forecasting has not been widely explored. Furthermore, most SVM models are applied for solving univariate forecasting problems. Therefore, this investigation examines the feasibility of SVMs with backpropagation neural networks in forecasting tourism demand influenced by different factors. A numerical example from an existing study is used to demonstrate the performance of tourist forecasting. Experimental results indicate that the proposed model outperforms other approaches for forecasting tourism demand.

The design of new Space Launch Vehicle (SLV) involves a full set of disciplines – propulsion, structural sizing, aerodynamics, mission analysis, flight control, stages layout – with strong interaction between each other. Since multidisciplinary design optimization of multistage launch vehicles is a complex and computationally expensive. An efficient Least Square Support Vector Regression (LS-SVR) technique is used for trajectory simulation of multistage space launch vehicle. This newly formulation problem-about 17 parameters, linked to both the architecture and the command (trajectory optimization), 8 constraints – is solved through hybrid optimization algorithm using Particle Swarm Optimization (PSO) as global optimizer and Sequential Quadratic Programming (SQP) as local optimizer starting from the solution given by (PSO). The objective is to find minimum gross launch weight (GLW) and optimal trajectory during launch maneuvering phase for liquid fueled space launch vehicle (SLV).The computational cost incurred is compared for two cases of conceptual design involving exact trajectory simulation and with Least Square Support Vector Regression based trajectory simulation.

Transductive inference estimates classification function at samples within the test data using information from both the training and the test data set. In this paper, a new algorithm of transductive support vector machine is proposed to improve Joachims’ transductive SVM to handle various data distributions. Simulated annealing heuristic is used to solve the combinatorial optimization problem of TSVM, in order to avoid the problems of having to estimate the ratio of positive/negative samples and local optimum. The experimental result shows that TSVM-SA algorithm outperforms Joachims’ TSVM, especially when there is a significant deviation between the distribution of training and test data.

Recently, multi-class SVMs have attracted much attention due to immense demands in real applications. Both the encoding and decoding strategies critically influence the effectiveness of the multi-class SVMs. In this work, a multi-class SVMs based on the SOM decoding algorithm is proposed. First, the binary SVM classifiers are trained according to the ECOC codes. Then the SOM network is trained with the output of the training samples and the optimum weights are obtained. Finally the unknown data is classified. By this method, the confidence of the binary classifiers is completely considered with the case avoided that the same minimum distance to several classes is obtained. The experimental results on the Yale face database demonstrate the superiority of the new algorithm over the widely-used Hamming decoding method.

XML has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Self Adaptive Migration Model Genetic Algorithm (SAMGA)[5] and multi class Support Vector Machine (SVM) are used to learn a user model. Based on the feedback from the users the system automatically adapts to the user’s preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.